code: def selection(
      acquisition_function,
      samples_y_aggregation,
      x_bounds,
      x_types,
      regressor_gp,
      minimize_starting_points,
      minimize_constraints_fun=None,
  ):
      """
      selection
      """
      outputs = None
  
      sys.stderr.write(
          '[%s] Exercise "%s" acquisition function\n'
          % (os.path.basename(__file__), acquisition_function)
      )
  
      if acquisition_function == "ei":
          outputs = lib_acquisition_function.next_hyperparameter_expected_improvement(
              gp_prediction.predict,
              [regressor_gp],
              x_bounds,
              x_types,
              samples_y_aggregation,
              minimize_starting_points,
              minimize_constraints_fun=minimize_constraints_fun,
          )
      elif acquisition_function == "lc":
          outputs = lib_acquisition_function.next_hyperparameter_lowest_confidence(
              gp_prediction.predict,
              [regressor_gp],
              x_bounds,
              x_types,
              minimize_starting_points,
              minimize_constraints_fun=minimize_constraints_fun,
          )
      elif acquisition_function == "lm":
          outputs = (
              lib_acquisition_function.next_hyperparameter_lowest_mu(
                  gp_prediction.predict,
                  [regressor_gp],
                  x_bounds,
                  x_types,
                  minimize_starting_points,
                  minimize_constraints_fun=minimize_constraints_fun,
              )
          )
      return outputs
  
file_path: nni/nni/algorithms/hpo/metis_tuner/Regression_GP/Selection.py
imports: import os
  import random
  import sys
  from ..None import lib_acquisition_function
  from ..None import lib_constraint_summation
  from ..None import lib_data
  from .None import Prediction
label_desc: Use image node_id 2 for calling a global function with example usage: selection(acquisition_function, samples_y_aggregation, x_bounds, x_types, regressor_gp, minimize_starting_points, minimize_constraints_fun) and returns: outputs